Face recognition technology requires a face detection technology as a pre-processing step. The face detection technology is implemented by discriminating face patterns and non-face patterns from an acquired face image.
To implement the face detection technology in hardware, it is required to design a database storing face and non-face pattern information and a look-up table having cost values of face features. Here, a cost value is a prediction value indicating the possibility that a face is present as a numerical value on the basis of statistical information that is autonomously collected.
By establishing the look-up table and the database having a large amount of face pattern information, the face detection technology provides superior face detection performance. Examples of face detection technology include a skin color-based approach, a Support Vector Machine (SVM) approach, a gaussian mixture approach, a maximum likelihood approach, and a neural network approach.
If such approaches use a database having a large amount of face information and a look-up table storing the cost values of face features, they ensure relatively high detection performance. However, the approaches cannot ensure real-time face detection performance due to the time taken in accessing the look-up table, image scaling, and excessive addition operations.
Recently, research has been conducted on intelligent robots to which face recognition technology has been applied. In the development of intelligent robots to which face recognition technology is applied, various kinds of sensor technologies are required. Intelligent robots require a distance sensing system for avoiding collisions and sensing obstacles, a position estimating system for detecting positions, and a visual sensing system for acquiring visual information. Among these systems, the visual sensing system that includes a visual sensor is important in performing the perception functions of a robot.
The visual sensor acquires images in real-time through a camera and converts the acquired images into image data. The visual sensor processes the image data in real-time and analyzes information on the size, position and color of an object (for example, a person or a face), and distance to the object to perceive the object. Here, the distance information between the object and the visual sensor may be obtained through a stereo matching operation.
FIG. 1 is a diagram illustrating the geometrical structure of a stereo matching system which performs a stereo matching operation.
Referring to FIG. 1, if a baseline B between the lenses 12 and 14 of two separated cameras, a disparity between objects in two images which are acquired from the respective cameras and a lens focal length F are given, a distance R between an object 22 and each of the camera lenses 12 and 14 can be obtained using Equation (1) below, based on the geometrical structure of FIG. 1.
                    R        =                  F          ⁢                      B            D                                              (        1        )            where R is distance between an object and a camera, B is the length of a baseline between the lenses of two cameras, F is a focal length of a camera lens, and D is distance difference, which is “DP1−DP2”, between the same object in two images.
The two image processing functions, that is, the face detection technology and the stereo matching operation technology are core technologies in human-robot interaction. However, it is difficult to concurrently implement the face detection technology and the stereo matching operation technology in low-performance systems such as non-robot small mobile devices, due to large operation volumes.